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An Integrated Approach for Microprotein Identification and Sequence Analysis

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A Comprehensive Survey of Deep Learning Techniques in Protein Function Prediction.

Richa Dhanuka, Jyoti Prakash Singh, Anushree Tripathi

IEEE/ACM Transactions on Computational Biology and Bioinformatics

|April 7, 2023

Summary

This survey reviews deep learning methods for protein function prediction, analyzing their evolution, accuracy, and limitations. It highlights the need for interpretable models in bioinformatics.

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Area of Science:

Bioinformatics
Computational Biology
Machine Learning

Background:

Protein function prediction is crucial in bioinformatics.
Diverse data types (sequences, structures, networks) are used.
High-throughput sequencing yields abundant protein sequence data.

Purpose of the Study:

To survey and systematically review deep learning techniques for protein function prediction.
To provide a chronological overview of advancing methodologies.
To identify current limitations and future research directions, focusing on model interpretability.

Main Methods:

Comprehensive literature review of deep learning methodologies for protein function prediction.
Analysis of various data representations including protein sequences.
Evaluation of proposed techniques based on pros, cons, and predictive accuracy.

Main Results:

Detailed overview of numerous deep learning techniques for protein function prediction.
Comparative analysis of methodologies, including their strengths and weaknesses.
Assessment of the predictive accuracy of different approaches.

Conclusions:

Deep learning on protein sequences is a promising approach for function prediction.
Existing methods require further development, particularly in model interpretability.
Future research should focus on creating explainable AI for biological insights.